Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations6014
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory751.8 KiB
Average record size in memory128.0 B

Variable types

Text1
Numeric10
Categorical4

Alerts

engine is highly overall correlated with max_power and 3 other fieldsHigh correlation
fuel is highly overall correlated with torque_rpm_minHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 3 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with max_power and 2 other fieldsHigh correlation
torque_Nm is highly overall correlated with engine and 2 other fieldsHigh correlation
torque_rpm_max is highly overall correlated with torque_rpm_minHigh correlation
torque_rpm_min is highly overall correlated with engine and 2 other fieldsHigh correlation
transmission is highly overall correlated with max_powerHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (68.2%) Imbalance
transmission is highly imbalanced (58.2%) Imbalance
torque_Nm is highly skewed (γ1 = 73.34774168) Skewed
max_power has 191 (3.2%) zeros Zeros
torque_Nm has 192 (3.2%) zeros Zeros
torque_rpm_min has 283 (4.7%) zeros Zeros
torque_rpm_max has 276 (4.6%) zeros Zeros

Reproduction

Analysis started2024-11-18 09:00:19.179436
Analysis finished2024-11-18 09:00:53.545705
Duration34.37 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:53.923002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length54
Median length43
Mean length25.166279
Min length11

Characters and Unicode

Total characters151350
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique905 ?
Unique (%)15.0%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti 1886
 
6.6%
hyundai 1083
 
3.8%
mahindra 629
 
2.2%
swift 592
 
2.1%
tata 544
 
1.9%
bsiv 507
 
1.8%
diesel 470
 
1.7%
1.2 435
 
1.5%
vxi 425
 
1.5%
vdi 420
 
1.5%
Other values (828) 21405
75.4%
2024-11-18T09:00:54.824421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 151350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22383
 
14.8%
a 11047
 
7.3%
i 10130
 
6.7%
t 7669
 
5.1%
r 6677
 
4.4%
o 5992
 
4.0%
n 5773
 
3.8%
e 5550
 
3.7%
u 4474
 
3.0%
S 4090
 
2.7%
Other values (58) 67565
44.6%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4475
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:55.146977image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0799204
Coefficient of variation (CV)0.0020263357
Kurtosis1.6891091
Mean2013.4475
Median Absolute Deviation (MAD)3
Skewness-1.0198946
Sum12108873
Variance16.64575
MonotonicityNot monotonic
2024-11-18T09:00:55.439462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2018 536
8.9%
2012 524
8.7%
2014 503
8.4%
2013 488
8.1%
2011 469
7.8%
2010 329
 
5.5%
2019 306
 
5.1%
Other values (19) 963
16.0%
ValueCountFrequency (%)
1983 1
 
< 0.1%
1991 1
 
< 0.1%
1994 3
 
< 0.1%
1995 1
 
< 0.1%
1996 3
 
< 0.1%
1997 10
0.2%
1998 9
0.1%
1999 12
0.2%
2000 19
0.3%
2001 7
 
0.1%
ValueCountFrequency (%)
2020 59
 
1.0%
2019 306
5.1%
2018 536
8.9%
2017 696
11.6%
2016 615
10.2%
2015 585
9.7%
2014 503
8.4%
2013 488
8.1%
2012 524
8.7%
2011 469
7.8%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521982.03
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:55.776489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile100000
Q1250000
median409999
Q3640000
95-th percentile1200000
Maximum10000000
Range9970001
Interquartile range (IQR)390000

Descriptive statistics

Standard deviation533842.62
Coefficient of variation (CV)1.0227222
Kurtosis52.713853
Mean521982.03
Median Absolute Deviation (MAD)190001
Skewness5.6236928
Sum3.1391999 × 109
Variance2.8498794 × 1011
MonotonicityNot monotonic
2024-11-18T09:00:56.103970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 188
 
3.1%
350000 173
 
2.9%
400000 147
 
2.4%
250000 144
 
2.4%
550000 142
 
2.4%
600000 142
 
2.4%
500000 138
 
2.3%
450000 137
 
2.3%
200000 133
 
2.2%
650000 125
 
2.1%
Other values (627) 4545
75.6%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 2
 
< 0.1%
31504 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 12
0.2%
42000 2
 
< 0.1%
45000 16
0.3%
45957 1
 
< 0.1%
50000 15
0.2%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 3
< 0.1%
5923000 1
 
< 0.1%
5850000 1
 
< 0.1%
5830000 1
 
< 0.1%
5800000 2
< 0.1%
5500000 3
< 0.1%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73764.15
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:56.415846image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11325
Q139000
median70000
Q3100000
95-th percentile155000
Maximum2360457
Range2360456
Interquartile range (IQR)61000

Descriptive statistics

Standard deviation59610.747
Coefficient of variation (CV)0.80812627
Kurtosis416.83816
Mean73764.15
Median Absolute Deviation (MAD)30000
Skewness12.58954
Sum4.436176 × 108
Variance3.5534411 × 109
MonotonicityNot monotonic
2024-11-18T09:00:56.748908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 446
 
7.4%
70000 373
 
6.2%
80000 371
 
6.2%
60000 342
 
5.7%
50000 313
 
5.2%
100000 290
 
4.8%
90000 280
 
4.7%
40000 238
 
4.0%
110000 229
 
3.8%
30000 192
 
3.2%
Other values (817) 2940
48.9%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 5
0.1%
1300 1
 
< 0.1%
1303 1
 
< 0.1%
1500 2
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 6
0.1%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
370000 1
< 0.1%

fuel
Categorical

High correlation 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Diesel
3269 
Petrol
2660 
CNG
 
51
LPG
 
34

Length

Max length6
Median length6
Mean length5.9575989
Min length3

Characters and Unicode

Total characters35829
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 3269
54.4%
Petrol 2660
44.2%
CNG 51
 
0.8%
LPG 34
 
0.6%

Length

2024-11-18T09:00:57.042077image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T09:00:57.304340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3269
54.4%
petrol 2660
44.2%
cng 51
 
0.8%
lpg 34
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 35829
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9198
25.7%
l 5929
16.5%
D 3269
 
9.1%
i 3269
 
9.1%
s 3269
 
9.1%
P 2694
 
7.5%
t 2660
 
7.4%
r 2660
 
7.4%
o 2660
 
7.4%
G 85
 
0.2%
Other values (3) 136
 
0.4%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Individual
5394 
Dealer
595 
Trustmark Dealer
 
25

Length

Max length16
Median length10
Mean length9.6291985
Min length6

Characters and Unicode

Total characters57910
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 5394
89.7%
Dealer 595
 
9.9%
Trustmark Dealer 25
 
0.4%

Length

2024-11-18T09:00:57.559334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T09:00:57.802612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 5394
89.3%
dealer 620
 
10.3%
trustmark 25
 
0.4%

Most occurring characters

ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57910
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 10788
18.6%
i 10788
18.6%
a 6039
10.4%
l 6014
10.4%
u 5419
9.4%
I 5394
9.3%
v 5394
9.3%
n 5394
9.3%
e 1240
 
2.1%
r 670
 
1.2%
Other values (7) 770
 
1.3%

transmission
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
Manual
5505 
Automatic
 
509

Length

Max length9
Median length6
Mean length6.2539075
Min length6

Characters and Unicode

Total characters37611
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 5505
91.5%
Automatic 509
 
8.5%

Length

2024-11-18T09:00:58.056063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T09:00:58.287364image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 5505
91.5%
automatic 509
 
8.5%

Most occurring characters

ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11519
30.6%
u 6014
16.0%
M 5505
14.6%
n 5505
14.6%
l 5505
14.6%
t 1018
 
2.7%
A 509
 
1.4%
o 509
 
1.4%
m 509
 
1.4%
i 509
 
1.4%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size94.0 KiB
First Owner
3721 
Second Owner
1691 
Third Owner
457 
Fourth & Above Owner
 
141
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.49418
Min length11

Characters and Unicode

Total characters69126
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner 3721
61.9%
Second Owner 1691
28.1%
Third Owner 457
 
7.6%
Fourth & Above Owner 141
 
2.3%
Test Drive Car 4
 
0.1%

Length

2024-11-18T09:00:58.519722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-18T09:00:58.769633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 6010
48.8%
first 3721
30.2%
second 1691
 
13.7%
third 457
 
3.7%
fourth 141
 
1.1%
141
 
1.1%
above 141
 
1.1%
test 4
 
< 0.1%
drive 4
 
< 0.1%
car 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 69126
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10337
15.0%
e 7850
11.4%
n 7701
11.1%
6300
9.1%
O 6010
8.7%
w 6010
8.7%
i 4182
6.0%
t 3866
 
5.6%
F 3862
 
5.6%
s 3725
 
5.4%
Other values (14) 9283
13.4%

mileage
Real number (ℝ)

Distinct379
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.461072
Minimum0
Maximum42
Zeros14
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:59.065265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.9585
Q117
median19.326154
Q322.32
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation3.9887158
Coefficient of variation (CV)0.20495869
Kurtosis0.82003735
Mean19.461072
Median Absolute Deviation (MAD)2.6738462
Skewness-0.16888122
Sum117038.89
Variance15.909854
MonotonicityNot monotonic
2024-11-18T09:00:59.394065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.9 184
 
3.1%
19.32615385 156
 
2.6%
19.7 146
 
2.4%
18.6 130
 
2.2%
21.1 124
 
2.1%
17 108
 
1.8%
15.96 98
 
1.6%
17.8 90
 
1.5%
16.1 86
 
1.4%
15.1 79
 
1.3%
Other values (369) 4813
80.0%
ValueCountFrequency (%)
0 14
0.2%
9 4
 
0.1%
9.5 1
 
< 0.1%
10 2
 
< 0.1%
10.1 2
 
< 0.1%
10.5 14
0.2%
10.71 1
 
< 0.1%
10.75 1
 
< 0.1%
10.8 1
 
< 0.1%
10.9 4
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.44 2
 
< 0.1%
33 1
 
< 0.1%
32.52 1
 
< 0.1%
30.46 2
 
< 0.1%
28.4 74
1.2%
28.09 29
 
0.5%
27.62 5
 
0.1%
27.4 4
 
0.1%
27.39 21
 
0.3%

engine
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1426.3133
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:00:59.720740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31498
95-th percentile2499
Maximum3604
Range2980
Interquartile range (IQR)301

Descriptive statistics

Standard deviation484.68346
Coefficient of variation (CV)0.33981557
Kurtosis1.1282984
Mean1426.3133
Median Absolute Deviation (MAD)213
Skewness1.2635795
Sum8577848
Variance234918.06
MonotonicityNot monotonic
2024-11-18T09:01:00.036576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 949
 
15.8%
1197 604
 
10.0%
796 360
 
6.0%
998 343
 
5.7%
1498 292
 
4.9%
2179 290
 
4.8%
1396 234
 
3.9%
1199 167
 
2.8%
2523 160
 
2.7%
1461 148
 
2.5%
Other values (116) 2467
41.0%
ValueCountFrequency (%)
624 16
 
0.3%
793 5
 
0.1%
796 360
6.0%
799 61
 
1.0%
814 92
 
1.5%
909 2
 
< 0.1%
936 29
 
0.5%
993 24
 
0.4%
995 40
 
0.7%
998 343
5.7%
ValueCountFrequency (%)
3604 1
 
< 0.1%
3498 1
 
< 0.1%
3198 2
 
< 0.1%
2999 2
 
< 0.1%
2997 2
 
< 0.1%
2993 12
0.2%
2987 8
 
0.1%
2982 23
0.4%
2967 8
 
0.1%
2956 14
0.2%

max_power
Real number (ℝ)

High correlation  Zeros 

Distinct313
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.123178
Minimum0
Maximum400
Zeros191
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:01:00.363263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.48
Q167.05
median81.8
Q399
95-th percentile147.835
Maximum400
Range400
Interquartile range (IQR)31.95

Descriptive statistics

Standard deviation35.048717
Coefficient of variation (CV)0.41174117
Kurtosis4.4179195
Mean85.123178
Median Absolute Deviation (MAD)14.8
Skewness1.0380631
Sum511930.8
Variance1228.4126
MonotonicityNot monotonic
2024-11-18T09:01:00.704699image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 282
 
4.7%
0 191
 
3.2%
88.5 168
 
2.8%
67 138
 
2.3%
46.3 136
 
2.3%
81.8 120
 
2.0%
62.1 119
 
2.0%
67.1 119
 
2.0%
67.04 117
 
1.9%
70 113
 
1.9%
Other values (303) 4511
75.0%
ValueCountFrequency (%)
0 191
3.2%
32.8 2
 
< 0.1%
34.2 18
 
0.3%
35 14
 
0.2%
35.5 2
 
< 0.1%
37 71
 
1.2%
37.48 8
 
0.1%
37.5 6
 
0.1%
38 1
 
< 0.1%
38.4 2
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 1
 
< 0.1%
272 1
 
< 0.1%
270.9 3
< 0.1%
265 1
 
< 0.1%
261.4 4
0.1%
258 2
< 0.1%
254.8 3
< 0.1%
254.79 1
 
< 0.1%

seats
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4238444
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:01:00.994618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9789588
Coefficient of variation (CV)0.18049168
Kurtosis4.0881246
Mean5.4238444
Median Absolute Deviation (MAD)0
Skewness2.0143169
Sum32619
Variance0.95836034
MonotonicityNot monotonic
2024-11-18T09:01:01.598970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 4762
79.2%
7 820
 
13.6%
8 196
 
3.3%
4 97
 
1.6%
9 69
 
1.1%
6 49
 
0.8%
10 18
 
0.3%
2 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 97
 
1.6%
5 4762
79.2%
6 49
 
0.8%
7 820
 
13.6%
8 196
 
3.3%
9 69
 
1.1%
10 18
 
0.3%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 18
 
0.3%
9 69
 
1.1%
8 196
 
3.3%
7 820
 
13.6%
6 49
 
0.8%
5 4762
79.2%
4 97
 
1.6%
2 2
 
< 0.1%

torque_Nm
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct226
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.55135
Minimum0
Maximum38038.7
Zeros192
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:01:01.893777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.5
Q190
median145
Q3200
95-th percentile330
Maximum38038.7
Range38038.7
Interquartile range (IQR)110

Descriptive statistics

Standard deviation497.65903
Coefficient of variation (CV)3.0615497
Kurtosis5582.7085
Mean162.55135
Median Absolute Deviation (MAD)55
Skewness73.347742
Sum977583.8
Variance247664.51
MonotonicityNot monotonic
2024-11-18T09:01:02.252479image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 517
 
8.6%
190 483
 
8.0%
90 304
 
5.1%
114 196
 
3.3%
0 192
 
3.2%
113 154
 
2.6%
160 141
 
2.3%
62 139
 
2.3%
250 115
 
1.9%
69 111
 
1.8%
Other values (216) 3662
60.9%
ValueCountFrequency (%)
0 192
3.2%
4.8 1
 
< 0.1%
5.7 1
 
< 0.1%
6.1 12
 
0.2%
7.8 5
 
0.1%
8.5 9
 
0.1%
8.6 6
 
0.1%
9.2 1
 
< 0.1%
9.8 9
 
0.1%
10.2 1
 
< 0.1%
ValueCountFrequency (%)
38038.7 1
 
< 0.1%
789 3
< 0.1%
640 1
 
< 0.1%
620 6
0.1%
619 3
< 0.1%
600 3
< 0.1%
580 2
 
< 0.1%
560 2
 
< 0.1%
550 6
0.1%
540 1
 
< 0.1%

torque_rpm_min
Real number (ℝ)

High correlation  Zeros 

Distinct62
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2474.6432
Minimum0
Maximum5000
Zeros283
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:01:02.584720image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200
Q11750
median2000
Q33500
95-th percentile4500
Maximum5000
Range5000
Interquartile range (IQR)1750

Descriptive statistics

Standard deviation1253.6837
Coefficient of variation (CV)0.5066119
Kurtosis-0.90316054
Mean2474.6432
Median Absolute Deviation (MAD)600
Skewness0.13676535
Sum14882504
Variance1571722.8
MonotonicityNot monotonic
2024-11-18T09:01:02.926426image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1750 865
14.4%
4000 691
11.5%
2000 650
10.8%
3500 589
 
9.8%
1500 319
 
5.3%
1400 284
 
4.7%
0 283
 
4.7%
1800 273
 
4.5%
3000 266
 
4.4%
2500 198
 
3.3%
Other values (52) 1596
26.5%
ValueCountFrequency (%)
0 283
4.7%
21 1
 
< 0.1%
100 9
 
0.1%
175 1
 
< 0.1%
200 17
 
0.3%
250 1
 
< 0.1%
300 1
 
< 0.1%
400 4
 
0.1%
500 89
 
1.5%
600 6
 
0.1%
ValueCountFrequency (%)
5000 20
 
0.3%
4850 19
 
0.3%
4800 73
1.2%
4750 7
 
0.1%
4700 7
 
0.1%
4600 49
 
0.8%
4500 159
2.6%
4400 55
 
0.9%
4388 1
 
< 0.1%
4386 60
 
1.0%

torque_rpm_max
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2815.6864
Minimum0
Maximum5300
Zeros276
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size94.0 KiB
2024-11-18T09:01:03.237363image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile465
Q12000
median2800
Q33750
95-th percentile4500
Maximum5300
Range5300
Interquartile range (IQR)1750

Descriptive statistics

Standard deviation1151.1097
Coefficient of variation (CV)0.40882028
Kurtosis-0.0026671182
Mean2815.6864
Median Absolute Deviation (MAD)800
Skewness-0.53267712
Sum16933538
Variance1325053.5
MonotonicityNot monotonic
2024-11-18T09:01:03.611288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000 724
12.0%
2000 650
10.8%
3500 573
 
9.5%
3000 483
 
8.0%
1750 444
 
7.4%
2500 416
 
6.9%
2750 337
 
5.6%
2800 321
 
5.3%
0 276
 
4.6%
4200 189
 
3.1%
Other values (48) 1601
26.6%
ValueCountFrequency (%)
0 276
4.6%
100 9
 
0.1%
200 11
 
0.2%
250 1
 
< 0.1%
300 1
 
< 0.1%
400 3
 
< 0.1%
500 100
 
1.7%
600 6
 
0.1%
700 28
 
0.5%
750 6
 
0.1%
ValueCountFrequency (%)
5300 1
 
< 0.1%
5200 1
 
< 0.1%
5000 22
 
0.4%
4850 19
 
0.3%
4800 73
1.2%
4750 7
 
0.1%
4700 7
 
0.1%
4600 50
 
0.8%
4500 165
2.7%
4400 55
 
0.9%

Interactions

2024-11-18T09:00:48.846552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:21.165563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:27.665191image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:30.278978image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:32.843030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:35.139018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:38.212903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:41.669826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:44.077076image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:46.526599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:49.082611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:21.622225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:28.135304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:30.551025image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:33.071188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:35.408306image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:38.578732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:41.902480image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:44.332734image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:46.761798image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:49.322731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:22.010013image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:28.406724image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:30.773016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:33.294199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:35.669492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:38.927409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:42.152184image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:44.595555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:46.979913image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:49.850791image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:22.511245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:28.620615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:30.985296image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:33.544757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:35.895965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:39.232292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:42.399680image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:44.814249image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:47.225348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:50.086919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:23.518772image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:28.832970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:31.191314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:33.745534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:36.121611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:39.563010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:42.623262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:45.059463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:47.450608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:50.426320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:24.746618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:29.064654image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:31.453933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:33.965851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:36.496261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:39.956824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:42.852444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:45.300237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:47.681332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:50.738963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:25.754982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:29.327947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:31.720736image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:34.203898image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:36.839383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:40.222018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:43.110672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:45.579634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:47.916260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:51.087801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:26.436171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:29.568221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:31.943806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:34.469848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:37.163209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:40.469102image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:43.346279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:45.813484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:48.159401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:51.465028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:26.889101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:29.813498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:32.201058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:34.712157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:37.505731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:41.100878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:43.625688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:46.048176image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:48.400552image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:51.814915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:27.312780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:30.043538image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:32.630395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:34.926764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:37.868126image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:41.417533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:43.841327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:46.291911image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-18T09:00:48.625156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-18T09:01:03.997237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetorque_Nmtorque_rpm_maxtorque_rpm_mintransmissionyear
engine1.0000.4470.3060.688-0.4300.0760.5260.0990.4650.657-0.343-0.5530.400-0.040
fuel0.4471.0000.0360.1450.2900.0240.2140.0470.1010.0000.4530.5320.0350.134
km_driven0.3060.0361.0000.014-0.1990.0330.1940.000-0.2940.074-0.363-0.3490.016-0.573
max_power0.6880.1450.0141.000-0.2870.1010.3150.1600.6440.7110.069-0.1910.5130.225
mileage-0.4300.290-0.199-0.2871.0000.095-0.4350.0390.028-0.033-0.0820.1500.2490.349
owner0.0760.0240.0330.1010.0951.0000.0210.1320.3630.0370.1230.1380.1180.259
seats0.5260.2140.1940.315-0.4350.0211.0000.0220.3200.375-0.123-0.3660.0330.050
seller_type0.0990.0470.0000.1600.0390.1320.0221.0000.1610.0000.0710.0990.2170.103
selling_price0.4650.101-0.2940.6440.0280.3630.3200.1611.0000.6490.035-0.1620.4650.705
torque_Nm0.6570.0000.0740.711-0.0330.0370.3750.0000.6491.000-0.188-0.3090.0140.307
torque_rpm_max-0.3430.453-0.3630.069-0.0820.123-0.1230.0710.035-0.1881.0000.7660.1270.243
torque_rpm_min-0.5530.532-0.349-0.1910.1500.138-0.3660.099-0.162-0.3090.7661.0000.1310.163
transmission0.4000.0350.0160.5130.2490.1180.0330.2170.4650.0140.1270.1311.0000.153
year-0.0400.134-0.5730.2250.3490.2590.0500.1030.7050.3070.2430.1630.1531.000

Missing values

2024-11-18T09:00:52.295232image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-18T09:00:53.127193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatstorque_Nmtorque_rpm_mintorque_rpm_max
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner23.401248.074.005.0190.02000.02000.0
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner21.141498.0103.525.0250.01500.02500.0
2Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner23.001396.090.005.022.41750.02750.0
3Maruti Swift VXI BSIII2007130000120000PetrolIndividualManualFirst Owner16.101298.088.205.011.5500.0500.0
4Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner20.141197.081.865.0113.74000.04000.0
5Maruti Wagon R LXI DUO BSIII200796000175000LPGIndividualManualFirst Owner17.301061.057.505.07.8500.0500.0
6Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner16.10796.037.004.059.02500.02500.0
7Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner23.591364.067.105.0170.01800.02400.0
8Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner20.001399.068.105.0160.02000.02000.0
9Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner19.011461.0108.455.0248.02250.02250.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powerseatstorque_Nmtorque_rpm_mintorque_rpm_max
6986Maruti Alto LXi201120000073000PetrolIndividualManualFirst Owner19.70796.046.305.062.03000.03000.0
6987Maruti 800 AC199740000120000PetrolIndividualManualFirst Owner16.10796.037.004.059.02500.02500.0
6988Maruti Alto K10 VXI Airbag201734000045000PetrolIndividualManualFirst Owner23.95998.067.105.090.03500.03500.0
6990Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner18.501197.082.855.0113.74000.04000.0
6991Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner20.51998.067.045.090.03500.03500.0
6992Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner17.921086.062.105.096.13000.03000.0
6993Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner18.90998.067.105.090.03500.03500.0
6994Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner18.501197.082.855.0113.74000.04000.0
6995Hyundai Verna CRDi SX2007135000119000DieselIndividualManualFourth & Above Owner16.801493.0110.005.024.0750.0900.0
6996Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner19.301248.073.905.0190.02000.02000.0